This is an introductory course in using modern data analysis concepts and tools to gain insight and make decisions in a business or organizational setting. Topics include data storage, business intelligence, basic data mining and modeling, visualization, prediction/forecasting, and clustering/segmentation. Students will complete at least one data analytics project, starting from an original research question and concluding with actionable recommendations.
An introduction to the construction and analysis of least-squares models, including multiple regression, ANOVA, ANCOVA, and mixed models. Generalized linear models will also be presented, with special attention paid to logistic regression and log-linear models. Examples and applications will be drawn from various disciplines, including biology, medicine, economics, engineering, and the social sciences.
Forecasting is the science of predicting future events and outcomes. In this course students will learn how to effectively use both data and theory to create forecasts and how to quantify and communicate uncertainty in forecasts. Topics include random walks, Markov models, time series analysis, Bayesian methods and qualitative forecasting.
Data mining is the study of discovering and assessing patterns, relationships and information within large datasets. This course provides an introduction to data mining with an emphasis on predictive modeling techniques and machine learning algorithms. Examples and applications will be drawn from various disciplines.
Courses covering various topics of interest in this particular discipline are offered regularly. Contact department or program chair for more information.
This course provides an opportunity for individual students to conduct in-depth study of a particular topic under the supervision of a faculty member. Contact the department or program chair for more information.
This course provides an opportunity for individual students to conduct research in a specific area of study, completed under the direction of a faculty mentor. Specific expectations of the research experience to be determined by the faculty. Repeatable for credit. Prerequisite: consent of instructor.